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In: NBER working paper series 11910
In: Understanding Poverty, S. 379-388
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In: University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2021-112
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In: NBER Working Paper No. w25854
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Working paper
In: NBER Working Paper No. w26168
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Working paper
In: University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2019-77
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Working paper
In: American economic review, Band 107, Heft 5, S. 476-480
ISSN: 1944-7981
Machine learning tools are beginning to be deployed en masse in health care. While the statistical underpinnings of these techniques have been questioned with regard to causality and stability, we highlight a different concern here, relating to measurement issues. A characteristic feature of health data, unlike other applications of machine learning, is that neither y nor x is measured perfectly. Far from a minor nuance, this can undermine the power of machine learning algorithms to drive change in the health care system--and indeed, can cause them to reproduce and even magnify existing errors in human judgment.
In: Review of Income and Wealth, Band 60, Heft 1, S. 7-35
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In: NBER Working Paper No. w18373
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